{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 倒入标准库\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "np.set_printoptions(suppress=True)\n",
    "from sklearn.metrics import accuracy_score, log_loss,confusion_matrix,precision_score,recall_score,roc_curve, auc\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>76000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15728773</td>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15598044</td>\n",
       "      <td>Female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15694829</td>\n",
       "      <td>Female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15600575</td>\n",
       "      <td>Male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15727311</td>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15570769</td>\n",
       "      <td>Female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15606274</td>\n",
       "      <td>Female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15746139</td>\n",
       "      <td>Male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15704987</td>\n",
       "      <td>Male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15628972</td>\n",
       "      <td>Male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15697686</td>\n",
       "      <td>Male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15733883</td>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15617482</td>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15704583</td>\n",
       "      <td>Male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15621083</td>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     User ID  Gender   Age  EstimatedSalary  Purchased\n",
       "0   15624510    Male  19.0          19000.0          0\n",
       "1   15810944    Male  35.0          20000.0          0\n",
       "2   15668575  Female  26.0          43000.0          0\n",
       "3   15603246  Female  27.0          57000.0          0\n",
       "4   15804002    Male  19.0          76000.0          0\n",
       "5   15728773    Male  27.0          58000.0          0\n",
       "6   15598044  Female  27.0          84000.0          0\n",
       "7   15694829  Female  32.0         150000.0          1\n",
       "8   15600575    Male  25.0          33000.0          0\n",
       "9   15727311  Female  35.0          65000.0          0\n",
       "10  15570769  Female  26.0          80000.0          0\n",
       "11  15606274  Female  26.0          52000.0          0\n",
       "12  15746139    Male  20.0          86000.0          0\n",
       "13  15704987    Male  32.0          18000.0          0\n",
       "14  15628972    Male  18.0          82000.0          0\n",
       "15  15697686    Male  29.0          80000.0          0\n",
       "16  15733883    Male  47.0          25000.0          1\n",
       "17  15617482    Male  45.0          26000.0          1\n",
       "18  15704583    Male  46.0          28000.0          1\n",
       "19  15621083  Female  48.0          29000.0          1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 倒入数据集\n",
    "dataset = pd.read_csv('./Social_Network_Ads.csv')\n",
    "dataset.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    204\n",
       "Male      196\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gender = dataset[\"Gender\"].values.reshape(-1, 1)\n",
    "Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gender = dataset[\"Gender\"].values.reshape(-1, 1)\n",
    "enc = OneHotEncoder(categories='auto').fit(Gender) \n",
    "\n",
    "Gender = enc.transform(Gender).toarray()\n",
    "Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   19., 19000.,     0.,     1.],\n",
       "       [   35., 20000.,     0.,     1.],\n",
       "       [   26., 43000.,     1.,     0.],\n",
       "       ...,\n",
       "       [   50., 20000.,     1.,     0.],\n",
       "       [   36., 33000.,     0.,     1.],\n",
       "       [   49., 36000.,     1.,     0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.iloc[:, [2, 3]].values\n",
    "X = np.append(X,Gender,axis=1)\n",
    "y = dataset.iloc[:, 4].values\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分离训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
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       "      <td>15810944</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>76000.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>15691863</td>\n",
       "      <td>Female</td>\n",
       "      <td>46.0</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15706071</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>15654296</td>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>Male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender   Age  EstimatedSalary  Purchased\n",
       "0    15624510    Male  19.0          19000.0          0\n",
       "1    15810944    Male  35.0          20000.0          0\n",
       "2    15668575  Female  26.0          43000.0          0\n",
       "3    15603246  Female  27.0          57000.0          0\n",
       "4    15804002    Male  19.0          76000.0          0\n",
       "..        ...     ...   ...              ...        ...\n",
       "395  15691863  Female  46.0          41000.0          1\n",
       "396  15706071    Male  51.0          23000.0          1\n",
       "397  15654296  Female  50.0          20000.0          1\n",
       "398  15755018    Male  36.0          33000.0          0\n",
       "399  15594041  Female  49.0          36000.0          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth is 7\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "testtr=[]\n",
    "testts=[]\n",
    "for i in range(20):\n",
    "    clf=DecisionTreeClassifier(max_depth=i+1,\n",
    "                                    criterion='entropy',\n",
    "                                    random_state=0,\n",
    "                                    splitter='random')\n",
    "    clf=clf.fit(X_train,y_train)\n",
    "    score1=clf.score(X_test,y_test)\n",
    "    score2=clf.score(X_train,y_train)\n",
    "    testts.append(score1)\n",
    "    testtr.append(score2)\n",
    "max_depth = testts.index(max(testts))+1\n",
    "print(f\"max_depth is {max_depth}\")\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.plot(range(1,21),testts,label='test_score',marker='*')\n",
    "plt.plot(range(1,21),testtr,label='train_score',marker='4')\n",
    "plt.scatter(testts.index(max(testts))+1,max(testts),marker='v',color='red',s=100)\n",
    "plt.text(testts.index(max(testts))+1,max(testts),'BEST max_depth=%d'%(testts.index(max(testts))+1))\n",
    "plt.grid(linestyle=\":\", color=\"r\")\n",
    "plt.legend()\n",
    "plt.title(\"score tend of MaxDepth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_samples_leaf is 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "testtr=[]\n",
    "testts=[]\n",
    "for i in range(120):\n",
    "    clf=DecisionTreeClassifier(min_samples_leaf=i+1,\n",
    "                                    max_depth=max_depth,\n",
    "                                    criterion='entropy',\n",
    "                                    random_state=0,\n",
    "                                    splitter='random')\n",
    "    clf=clf.fit(X_train,y_train)\n",
    "    score1=clf.score(X_test,y_test)\n",
    "    score2=clf.score(X_train,y_train)\n",
    "    testts.append(score1)\n",
    "    testtr.append(score2)\n",
    "    \n",
    "min_samples_leaf = testts.index(max(testts))+1\n",
    "print(f\"min_samples_leaf is {testts.index(max(testts))+1}\")\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.plot(range(1,121),testts,label='test_score',marker='*')\n",
    "plt.plot(range(1,121),testtr,label='train_score',marker='4')\n",
    "plt.scatter(testts.index(max(testts))+1,max(testts),marker='v',color='red',s=100)\n",
    "plt.text(testts.index(max(testts))+1,max(testts),'BEST min_samples_leaf=%d'%(testts.index(max(testts))+1))\n",
    "plt.grid(linestyle=\":\", color=\"r\")\n",
    "plt.legend()\n",
    "plt.title(\"score tend of min_samples_leaf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_samples_split is 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "testtr=[]\n",
    "testts=[]\n",
    "for i in np.arange(2,200,1):\n",
    "    clf=DecisionTreeClassifier(min_samples_split=i,\n",
    "                                    min_samples_leaf=min_samples_leaf,\n",
    "                                    max_depth=max_depth,\n",
    "                                    criterion='entropy',\n",
    "                                    random_state=0,\n",
    "                                    splitter='random')\n",
    "    clf=clf.fit(X_train,y_train)\n",
    "    score1=clf.score(X_test,y_test)\n",
    "    score2=clf.score(X_train,y_train)\n",
    "    testts.append(score1)\n",
    "    testtr.append(score2)\n",
    "    \n",
    "min_samples_split = testts.index(max(testts))+2\n",
    "print(f\"min_samples_split is {min_samples_split}\")\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.plot(np.arange(2,200,1),testts,label='test_score',marker='*')\n",
    "plt.plot(np.arange(2,200,1),testtr,label='train_score',marker='4')\n",
    "plt.scatter(testts.index(max(testts))+2,max(testts),marker='v',color='red',s=100)\n",
    "plt.text(testts.index(max(testts))+2.5,max(testts)+0.0001,'BEST min_samples_split=%d'%(testts.index(max(testts))+2))\n",
    "plt.grid(linestyle=\":\", color=\"r\")\n",
    "plt.legend()\n",
    "plt.title(\"score tend of min_samples_split\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================\n",
      "DecisionTreeClassifier\n",
      "****Results****\n",
      "Accuracy: 92.0000%\n",
      "Confusion Matrix:\n",
      " [[63  5]\n",
      " [ 3 29]]\n",
      "Error rate: 0.07999999999999996\n",
      "Precision Score: 0.92\n",
      "Recall Score: 0.92\n",
      "Log Loss: 2.4498031162183658\n",
      "==============================\n",
      "RandomForestClassifier\n",
      "****Results****\n",
      "Accuracy: 93.0000%\n",
      "Confusion Matrix:\n",
      " [[64  4]\n",
      " [ 3 29]]\n",
      "Error rate: 0.06999999999999995\n",
      "Precision Score: 0.93\n",
      "Recall Score: 0.93\n",
      "Log Loss: 0.8238718988470388\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dagoose\\AppData\\Local\\Temp\\ipykernel_8212\\1468541589.py:73: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  log = log.append(log_entry)\n",
      "C:\\Users\\dagoose\\AppData\\Local\\Temp\\ipykernel_8212\\1468541589.py:73: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  log = log.append(log_entry)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================\n",
      "XGBClassifier\n",
      "****Results****\n",
      "Accuracy: 92.0000%\n",
      "Confusion Matrix:\n",
      " [[64  4]\n",
      " [ 4 28]]\n",
      "Error rate: 0.07999999999999996\n",
      "Precision Score: 0.92\n",
      "Recall Score: 0.92\n",
      "Log Loss: 0.22870469517190942\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dagoose\\AppData\\Local\\Temp\\ipykernel_8212\\1468541589.py:73: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  log = log.append(log_entry)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "classifiers = [\n",
    "    DecisionTreeClassifier(max_depth=max_depth,min_samples_split=min_samples_split,min_samples_leaf=min_samples_leaf),\n",
    "    RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0),\n",
    "    XGBClassifier()\n",
    "    ]\n",
    "log_cols=[\"Classifier\",\"Accuracy\",\"Error rate\",\"Precision Score\",\"Recall Score\", \"Log Loss\"]\n",
    "log = pd.DataFrame(columns=log_cols)\n",
    "pred = []\n",
    "proda = []\n",
    "plt.figure(figsize=(20,20))\n",
    "i = 1\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, y_train)\n",
    "    name = clf.__class__.__name__\n",
    "\n",
    "    print(\"=\"*30)\n",
    "    print(name)\n",
    "    print('****Results****')\n",
    "\n",
    "    # 预测类型\n",
    "    train_predictions = clf.predict(X_test)\n",
    "    pred.append(train_predictions)\n",
    "\n",
    "    # 预测正确的概率\n",
    "    train_proba = clf.predict_proba(X_test)\n",
    "    proda.append(train_proba)\n",
    "\n",
    "    # 预测结果是否正确\n",
    "    res = y_test-train_predictions\n",
    "    res[res!=0]=1\n",
    "\n",
    "    # 获取每一个预测结果的概率\n",
    "    index_max = np.argmax(train_proba, axis=1)\n",
    "    max = train_proba[range(train_proba.shape[0]), index_max]\n",
    "\n",
    "    acc = accuracy_score(y_test, train_predictions)\n",
    "    print(\"Accuracy: {:.4%}\".format(acc))\n",
    "\n",
    "    cm = confusion_matrix(y_test, train_predictions)\n",
    "    print(f\"Confusion Matrix:\\n {cm}\")\n",
    "\n",
    "    err = 1 - acc\n",
    "    print(f\"Error rate: {err}\") \n",
    "\n",
    "    ps = precision_score(y_test, train_predictions ,average='micro')\n",
    "    print(f\"Precision Score: {ps}\")\n",
    "\n",
    "    rs = recall_score(y_test, train_predictions,average='micro')\n",
    "    print(f\"Recall Score: {rs}\")\n",
    " \n",
    "    ll = log_loss(y_test, train_proba)\n",
    "    print(\"Log Loss: {}\".format(ll))\n",
    "\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(res,max, pos_label=0, sample_weight=None, drop_intermediate=True)\n",
    "    roc_auc = auc(fpr, tpr)                                     #auc为Roc曲线下的面积\n",
    "    #开始画ROC曲线\n",
    "    \n",
    "    plt.subplot(3,3,i)\n",
    "    plt.tight_layout()\n",
    "    i+= 1\n",
    "    plt.plot(fpr, tpr, 'b',lw=2,label='AUC = %0.2f'% roc_auc)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.plot([0,1],[0,1],'r--',lw=2)\n",
    "    plt.xlim([-0.1,1.1])\n",
    "    plt.ylim([-0.1,1.1])\n",
    "    plt.xlabel('False Positive Rate')                            #横坐标是fpr\n",
    "    plt.ylabel('True Positive Rate')                             #纵坐标是tpr\n",
    "    plt.title(name)\n",
    "    \n",
    "    plt.legend(loc=\"lower right\")\n",
    "    log_entry = pd.DataFrame([[name, acc*100,err*100,ps*100,rs*100, ll,]], columns=log_cols)\n",
    "    log = log.append(log_entry)\n",
    "plt.show()"
   ]
  }
 ],
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   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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